In medical exams, ?incidental findings? are findings that should be noted but that are incidental to the purpose of the exam (e.g. looking for pneumonia and unexpectedly finding cancer). Missed incidental findings can have significant medical and legal consequences. These errors can be considered as ?satisfaction of search? errors and are closely related to ?inattentional blindness? in basic vision research. Such errors occur regularly in daily life, as when, after a search of the supermarket, you arrive home with the tomatoes, but not the basil. The goal of this proposal is to understand the conditions that produce incidental finding errors in socially-important search tasks and to use that understanding to generate interventions that can improve the efficiency of those searches and reduce failure rates. We will develop a laboratory model of incidental findings built on aspects of search behavior studied extensively in our lab in the prior grant period: First, we have studied ?hybrid search? tasks in which observers search for multiple types of target at the same time, combining visual search and memory search. Second, we pioneered the study of human ?foraging? search - search for an unknown numbers of instances of one target type (e.g. picking berries). Combining those two paradigms, we have studied ?hybrid foraging? in which observers search for an unknown number of instances of multiple targets (analogous to some medical situations). Finally, we have extensively studied `prevalence effects'; the finding that rare items are found less successfully in search, simply as a function of their low prevalence. We have documented these prevalence effects in the lab, in baggage screening and in cancer screening. Building on this prior work, there are three specific aims: 1) We will combine hybrid search, foraging, and low prevalence methods into a model system to test the hypothesis that the structure of some clinical search tasks makes it more likely that incidental findings will be missed. We will compare our model system to radiologists' behavior and we will assess interventions to reduce these false negative errors. 2) We will use a second model system to study the impact of vigilance/time-on-task and interruption. Here we use signal detection methods to distinguish changes in detectability from criterion shifts. 3) Our Guided Search (GS) model has been a leading account of search performance in classic search tasks. The third aim is to have a GS model that can predict conditions that will produce high rates of incidental finding errors. More importantly, the model will be a tool that can be used to identify testable conditions that might minimize those errors. Our experiments will provide insights into fundamental processes of visual search and can lead to interventions that can be used to reduce a dangerous type of medical error.